Wearable System for Evaluating Joint Performance and Function

ABSTRACT

One embodiment of a disclosed wearable plyowrap with a plurality of fluid embedded into the structure of the plyowrap to support a joint of the user. A brace feedback system receives sensor data describing the movement of the joint. The sensor data is collected by an array of tension sensors embedded into the plyowrap. The collected sensor data is input to a machine-learning model to generate a prediction of the performance of the joint and determine whether the joint is at risk of a biomechanical compromise based on the predicted performance of the joint. The brace feedback system generates instructions for adjusting structural properties of the plyowrap to improve performance of the joint by activating electroactive gels in fluid chambers of the plyowrap to adjust the pliability of the plyowrap. The brace feedback system transmits the instructions to a local controller coupled to the plyowrap.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 63/082,822, filed on Sep. 24, 2020, which is incorporated herein in its entirety for all purposes. This application is a continuation-in-part of co-pending U.S. application Ser. No. 17/351,157 filed on Jun. 17, 2021, which claims the benefit of U.S. Provisional Patent Application No. 63/041,627, filed on Jun. 19, 2020, both of which are incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosure relates generally to a wrap worn around a joint and, more specifically, to a form fitting dynamic sleeve integrated with a digital system for evaluating and monitoring the joint of a wearer.

BACKGROUND

Conventional flexible knee sleeves merely provide soft tissue support in the form of a wrap or fabric covering the knee. Although there are variations in the design, fabric, size, and support provided by such braces, they all passively providing comfort to the knee, but do not actually collect data regarding the condition, performance, movement, or function of the knee. Additionally, conventional rigid knee braces limit the range of motion of the knee. Additionally, these conventional braces are similarly incapable for collecting data regarding the knee because they are incompatible with sensors on collecting data on the knee or body of a wearer. That is, these braces do not adjust their functionality or performance based on feedback describing the current condition or performance of the knee. Additionally, because they are designed to accommodate typical use by a wearer and typical injury patterns, conventional rigid braces restrain a wearer's movement without regard for the patient's specific condition because they are designed to accommodate typical use by a wearer and typical injury patterns.

Further, conventional techniques and tools for evaluating the performance or function of a knee do not provide automatic and continuous feedback nor do they enable real-time adjustments to a knee brace. Measurements performed by tools such as a goniometer must be taken manually and are not designed to continuously collect data while a subject moves freely. Even in laboratories, electronic motion sensors only measure the motion of the knee in a single direction (i.e., flexion and extension) but are not designed for use during recreational activities given their cumbersome size.

Other conventional wearable sensors, which describe the performance of the knee based on the relative motion of the leg to the thigh, are inaccurate and imprecise for calibrating the kinematic function of the knee.

BRIEF DESCRIPTION OF DRAWINGS

The disclosed embodiments have other advantages and features which will be more readily apparent from the detailed description, the appended claims, and the accompanying figures (or drawings). A brief introduction of the figures is below.

FIG. 1 illustrates a system architecture for a digital brace system, according to one example embodiment.

FIG. 2 is an illustration of a knee wrap that includes a combination of electronic components that detect and analyze movements of the knee, according to one example embodiment.

FIG. 3A is an illustration of an array of tension sensors used to detect movements of the knee, according to one example embodiment.

FIGS. 3B-C are illustrations of a knee wrap with an array of the tension sensors anchored by a combination of circuit bands embedded into the knee wrap, according to one example embodiment

FIGS. 4A-I are illustrations of alternate embodiments of wraps worn around joints, according to one example embodiment.

FIG. 5 is an illustration of a knee wrap with electroactive fluid chambers, according to one example embodiment.

FIG. 6 is an illustration of a knee wrap with sensors configured to monitor physiological parameters and address changes in those parameters, according to one example embodiment.

FIG. 7a is an illustration of a cross-section of a knee wrap that includes an outer layer and an inner layer, according to one example embodiment.

FIG. 7B is an illustration of a cross-section of a knee wrap that includes an outer layer, an inner layer, and an alternate configuration of the tension sensor array, according to an example embodiment.

FIG. 8 is a block diagram of the system architecture of the brace feedback system, according to an embodiment.

FIG. 9 is an exemplary data flow for adjusting properties of the wrap to support a joint or muscle, according to an embodiment.

FIG. 10 is a block diagram illustrating components of an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller), according to one example embodiment.

The figures depict various embodiments of the presented invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.

DETAILED DESCRIPTION

The Figures (FIGS.) and the following description relate to preferred embodiments by way of illustration only. It should be noted that from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the principles of what is claimed.

Reference will now be made in detail to several embodiments, examples of which are illustrated in the accompanying figures. It is noted that wherever practicable similar or like reference numbers may be used in the figures and may indicate similar or like functionality. The figures depict embodiments of the disclosed system (or method) for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.

I. Overview

A digital brace system characterizes the motion of the joint itself and the body around the joint, for example by measuring the angular displacement of the joint in a particular plane or the pressure and force on the joint. In addition, as a user moves while wearing the brace, sensors integrated into the brace measure changes in physiological properties of the body around the joint and the joint itself. Such measurements and data may be stored locally on a controller embedded into the brace or remotely at a server in communication with the controller embedded into the brace.

The comprehensive data captured by the sensors provides precise information on gross and subtle movements of the joint and parts of the body surrounding the joint to provide feedback for adjusting the brace in real-time or near real-time to correct movement of the joint, detect irregular movements of the joint, or further increase performance of the joint in manner that conventional braces are incapable of. In particular, the brace includes additional components and elements that allow the brace to adjust its shape to accommodate the movement of the joint or to deliver a combination of nutrients to the user to maintain or improve the performance of the joint. The digital brace system described herein may be used in parallel with pertinent treatments to evaluate joint function over a long period of time.

II. System Configuration

As described herein, a plyowrap is a dynamic sleeve designed with a pliable material that is capable of adapting to the dynamic movement of a joint without inhibiting the movement of the joint. The plyowrap may be worn by a user around any joint to monitor, maintain, or improve the performance and function of the joint. For example, the plyowrap may measure aspects of joint motion, limb motion, spinal motion, or a combination thereof. In some example embodiments, the plyowrap identifies changes in the activity regarding a joint or neuromusculoskeletal function by generating a digital contour map of the joint and body status and monitoring movement and function at various points in time based on continuous electronic recordings. The wrap may be structured in a variety of ways, for example, as a rectangular structure that wraps around a knee or a sleeve that is pulled up to the knee from the foot. Alternative embodiments of the plyowrap are additionally described herein.

In some embodiments, the plyowrap may be configured with a support structure or frame to provide additional support to the joint or surrounding body parts. The frame may be coupled internally or externally the plyowrap. In such embodiments, the plyowrap may be referred to as a “wearable brace.”

FIG. 1 illustrates a system architecture for a digital brace system, according to one example embodiment. The digital brace system 100 may includes a plyowrap 110, a device feedback system 120, a patient device 130, a provider device 140, and a network 150. The wearable brace further includes one or more sensors 160 and a controller 170 embedded into the brace. Although FIG. 1 illustrates only a single instance of most of components of the digital brace system 100, in practice more than one of each component may be present, and additional or fewer components may be used.

Sensors 160 are embedded within the brace 110 to collect data describing various aspects of the motion of the joint and to characterize the movement of the joint based on kinematic information, gait information, balance information regarding joint and body alignment, and/or kinematic function and activity determined from the collected sensor data. The sensors 160 may be embedded into the wearable brace 110 by weaving them into the pliable wrap of the brace 110 or any other suitable material integration technique. In alternate embodiments, the sensors 160 are attached to the wrap using an adhesive or any other suitable attachment technique. In embodiments where the brace 110 is worn over the knee of a user, the sensors 160 additionally collect balance information of the tibio-femoral and tibio-fibular patellofemoral movements.

The controller 170 receives signals from the sensors 160 describing physiological properties of the user and the joint including, but not limited to temperature, O₂ saturation, glucose, energy, heat, elasticity, vibration, blood pressure, heart rate, conductivity, sensitivity, ultrasonic impedance, electromagnetic frequency, chemistry, or any other suitable physical or physiological properties, and changes in those physiological properties. Based on the received signals, the controller 170 encodes instructions for adjusting the wearable brace 110 into electronic signals and transmits those signals to other components of the wearable brace 110. As an example, the controller 170 may transmit an electronic signal that causes fluid chambers in the plyowrap to change geometry to increase or decrease the pliability of the plyowrap. As another example, the controller 170 may transmit electronic signals that cause fluid chambers in the plyowrap to change permeability to enable transdermal delivery of nutrients stored within the fluid chambers.

The design and functionality of the wearable brace 110, the sensors 160, and the controller 170 are further discussed below with reference to FIGS. 2-9.

When the wearable brace 110 is worn around a joint to measure neuromusculoskeletal function, the brace feedback system 120 analyzes movements of the joint based on changes in a contour digital map describing the movement and performance of the joint over time, measurements recorded by wired and wireless sensors, or a combination thereof. The brace feedback system 120 may analyze the measured data to represent the knee visually as an image (or an animation) or computationally in a dataset (or a pattern). In embodiments involving computational representations of the measured data, the datasets and patterns are collected sequentially and serially over time to provide analytics of the knee, which may be reviewed by a patient or medical provider. Alternatively, the collected analytics may be used as a basis for further individual and comparative analysis and pattern recognition. Applicable analytics techniques include, but are not limited to, cognitive learning, machine learning, augmented reality, and virtual reality systems. In embodiments where larger datasets of multiple individuals are available to the brace feedback system 120, the brace feedback system 120 may be trained to predict an irregular movement of the joint and to evaluate the performance of the joint based on patterns, characteristics, and outcomes derived from the larger dataset of multiple individuals. In alternate embodiments, the functionality described with reference to the brace feedback system 120 may be performed locally by the controller 170 and the data collected by the sensors 160 may be stored locally by the controller 170.

In some embodiments, the brace feedback system 120 may communicate with a patient device 130, a provider device 140, or both to display real-time feedback collected from the wearable brace to a user, a coach, a trainer, a therapist, or another third party. The data collected by the wearable brace 110 may provide an image of the movement of the knee in real-time with quantitative data and visually interpretable qualitative patterns of motion, which may be processed into a computational representation of the motion of the user's knee. The brace feedback system 120 may perform comparative analytics of the visual representations through any suitable technique including, but not limited to, computational, applied, analytic, or interpreted techniques, algorithmic learning, machine learning, artificial intelligence, or any other suitable technique. The brace feedback system 120 may visually display motion captured patterns of the anatomical movements of the knee using wire frame models or other computational 3D models of the actual knee motion. Accordingly, the brace feedback system 120 may develop or compute a representative model of the knee, which characterizes both the form and function of the knee in real-time.

In one embodiment, the brace feedback system 120 displays feedback using a color-coded system. A display of a first color (e.g., green) indicates that the pattern of motion is normally distributed with no tension or constraint beyond a set of normalized parameters. A display of a second color (e.g., yellow) indicates the presence of one or more tension or vibration parameters. Based on the presence of such parameters, the brace feedback system may alert the user that the joint may be biomechanically compromised given the motion or position of the joint, that the position or motion of the joint may cause use user to fall, that further movement may cause a biomechanical compromise the biomechanics of the joint, or a combination thereof. Alternatively, or in addition to, the display of the second color may indicate that an adjustment to the wearable brace 110 may be necessary. As discussed herein, a biomechanical compromise in the position or function of a joint occurs when the joint is predicted to move or function in a manner inconsistent with an expected function or position of the joint.

The brace feedback system 120 may additionally generate predictions using the motion data captured by the wearable brace 110, which includes the rotation, translation, glide, tilt, pressure, forces, velocity, and acceleration of the various anatomic components of the joint in space and relative to other anatomic or biomechanical components of the joint. The brace feedback system 120 may generate predictions based on the overall pattern of motion based on measures and analyses repeated over time for a particular user in a healthy state or alternate control state. The pattern of motion recorded for the joint may be correlated with physiologic parameters, which may be measured by physiological sensors in the wearable brace 110 (not shown) or determined based on threshold parameters determined from previously collected data and analyses.

The brace feedback system 120 may additionally process data collected by the wearable sensor data 110. The data may be applied to help predict, prevent, and/or support biomechanical compromises of the joint or muscle and/or optimize performance of the joint or muscle. To optimize performance of the joint or muscle, the brace feedback system 120 may communicate signals with instructions for the wearable brace 110 to activate electrochemical, electrohydraulic, hydraulic, mechanical components of the brace 110, an air bladder, an electroactive gel, or any other feature of the brace 110 suitable for improving the performance or biomechanical function of the joint or muscle. As described herein, biomechanical function of a joint may refer to the speed or velocity of the joint, strength of the joint, or any other pertinent measurable and alterable joint parameters.

For the sake of simplicity, embodiments of the digital brace system 100, and more specifically the wearable brace 110, are described herein with reference to a knee brace, but a person having ordinary skill in the art would appreciate that the described digital brace system 100 may be used to monitor the performance and function of any joint of the human body (e.g., elbows, ankles, wrists, shoulders, hips). In addition to wearable joint or body sleeves, the brace 110 may also be a glove, exosuit, or mechanical brace for therapeutic use. Because embodiments of a wearable brace 110 may be designed using a plyowrap without a frame, the knee brace itself may also be characterized as a “plyowrap.” Accordingly, the principles described herein in the context of a knee brace may be applied to embodiments of “wraps,” “plyowraps,” or “dynamic sleeves” worn by a user around the knee or in other contexts.

In some embodiments, the brace 110 may be worn around the face of a user (e.g., a mask), which appropriate structural channels to provide for inhaling and exhaling air. In such embodiments, the brace 110 may be configured to maintain oxygen flow through the airway when the user is unconscious or injured, sensor data indicates abnormal oxygen saturations, facial, neck, and chest patterns indicate a compromised upper airway, or a combination thereof.

In some embodiments (not shown), a single user may wear multiple wearable braces 110, for example braces 110 on both knees. In such embodiments, data recorded by each brace 110 may be aggregated into a combined dataset, which the brace feedback system 120 may use for a more holistic assessment of the user's body. The brace feedback system 120 may use the data in the combined dataset to perform a coordinated assessment of the user's motion, performance, and health.

The patient device 130 is a computing device through which a user wearing the brace 110 may interact with the digital brace system 100. Similarly, the provider device 140 is a computing device through which a medical provider or third-party entity overseeing the user wearing the brace 110 may interact with the digital brace system 100. The patient device 130 and the provider device 140 may be computer systems. An example physical implementation of such a system is more completely described with FIG. 7. Each of the patient device 130 and the provider device 140 are configured to communicate with the controller 170, the device feedback system 120, or both via the network 150. For example, via a wireless application stored on the patient device 130, a user can communicate instructions for the controller 170 to adjust the shape of the wearable brace 110 or to deliver particular nutrients to the user.

The patient device 130 may also store third-party health monitoring applications that monitor various related aspects of the health of a user. The wearable device 110 and the brace feedback system 120 may communicate with such third-party applications to gain further insight into a patient's health. For example, a third-party application stored on the patient device 130 may communicate with a heart rate monitor on the patient's chest. Accordingly, the digital brace system 100, and more specifically the brace feedback system 120 and the wearable brace 110, may synchronize with the third party application to determine when the patient is in discomfort or is experiencing a symptom and supplement the data collected by the sensors with those determinations.

Similarly, a provider, trainer, or supervisor of the user may operate the provider device 140 to communicate instructions to the controller based on feedback or data recorded by the sensors 160. Accordingly, the wearable brace 110 may be used as a diagnostic tool by healthcare providers who monitor data received from physiology sensors embedded in the plyowrap. Examples of other users operating a provider device 140 include, but are not limited to, an employer, a technician, a caregiver, a trainer, a coach, a physical therapist, a doctor, or a health care worker. The communication between the patient device 130 and the provider device 140 and other components of the digital brace system 100 may be wireless, for example, via a short-range communication protocol such as Bluetooth, cellular, Wi-Fi, Ant, Zigbee, Ultra-wide-band (UWB), or any other suitable wireless connection or long-range communication protocol (e.g., satellite-based radio transmitters at various frequencies such as long-range backscatter system, VHF, UHF or K-band, S- or X- or other such signals that can be received in space)

The network 150 represents the various wired and wireless communication pathways between the wearable brace 110, the device feedback system 120, the patient device 130, and the provider device 140 via network 150. Network 150 uses standard Internet communications technologies and/or protocols. Thus, the network 150 can include links using technologies such as Ethernet, IEEE 802.11, integrated services digital network (ISDN), asynchronous transfer mode (ATM), etc. Similarly, the networking protocols used on the network 150 can include the transmission control protocol/Internet protocol (TCP/IP), the hypertext transport protocol (HTTP), the simple mail transfer protocol (SMTP), the file transfer protocol (FTP), etc. The data exchanged over the network 150 can be represented using technologies and/or formats including the hypertext markup language (HTML), the extensible markup language (XML), a custom binary encoding etc. In addition, all or some links can be encrypted using conventional encryption technologies such as the secure sockets layer (SSL), Secure HTTP (HTTPS) and/or virtual private networks (VPNs). In another embodiment, the entities can use custom and/or dedicated data communications technologies instead of, or in addition to, the ones described above. The network 150 may enable components of the digital brace system 100 to communicate using wireless connections, for example Bluetooth, cellular, Wi-Fi, Ant, Zigbee, or any other suitable wireless connection or long-range communication protocol.

III. Structure of the Wearable Brace

To use the wearable device, a user secures a “plyowrap,” around the joint of interest. As discussed above, the plyowrap may be worn over any joint of the body, for example the knee, the elbow, the wrist, or any other suitable joint. For illustrative purposes, the following description discusses embodiments in which the wearable brace 110 is a plyowrap used to cover the knee of a user, hereafter referred to as a “knee wrap.” However, a person having ordinary skill in the art would appreciate that the description included herein may be applied to a plyowrap used to cover any other joint.

When worn by a user to cover any suitable joint, the wearable brace 110 will collect data regarding the performance and functionality of the joint itself and parts of the body surrounding the joint. In some embodiments, the plyowrap is a tubular sleeve that may be pulled over the joint using anatomic features surrounding the joint to guide the placement and fit of the plyowrap. In such embodiments, the plyowrap may include graphic markers describing the anatomic orientation of the plyowrap. Alternatively, the plyowrap may be worn by folding the plyowrap over the joint using a securing mechanism to secure the plyowrap in place, for example a buckle, a clasp, a removable adhesive, or any other suitable securing mechanism.

In embodiments involving a knee wrap as discussed above, the placement and fit of the plyowrap of the knee wrap may be guided using some combination of the patella, tibial tubercle, the medial and lateral condyles, and any other suitable anatomic features of the knee. In embodiments of a knee wrap, for example the knee wrap 350 illustrated in FIG. 3C, the wrap 350 may include an opening or a socket to accommodate the position of the patella within the wrap 350. The knee wrap may further be designed to expose the back of the knee to improve the comfort of the user. The knee wrap may additionally be designed with vent holes in the area of the wrap covering the knee or any other body part around the knee to improve the breathability of the wrap.

The flexible, pliable material of the plyowrap conforms, when worn, to the shape of the joint of the user. Given the pliable nature of the material, the size and geometry of the plyowrap may vary depending on how the plyowrap is stretched based on size of the joint covered by the plyowrap and the movement of the joint covered by the plyowrap. In some embodiments, the circumference of the plyowrap is initially 5 centimeters before being stretched. Similarly, the cross-sectional thickness of the plyowrap may vary depending on the joint being covered, the size of the joint being covered, or the purpose for which the plyowrap is being used. In other embodiments, the plyowrap may be designed to accommodate a particular body type (e.g., toddlers, children, adults) or a particular user (e.g., a wrap customized for the user).

The flexible, pliable material of the plyowrap may be a natural or synthetic fabric or set of woven fabrics. As an example, the material may be a blend of materials including, but not limited to, neoprene, lycra, viscose, polyester, rubber, and natural fibers such as silk, cotton, hemp, or wool. The plyowrap may further be breathable, moisture wicking, moisture resistant, moisture repellant, or a combination thereof. In some embodiments, the knee wrap is electrically conductive. Alternatively, particular components of the knee wrap may be conductive.

In some embodiments, a support structure (e.g., a support frame) is integrated into the material of the plyowrap to provide additional support to the joint covered by the plyowrap. The support structure may be rigid and durable enough to prevent unexpected or damaging movements of the joint, but malleable enough to allow the plyowrap to conform to natural movements of the joint within the plyowrap. The support structure may have mechanical, hydraulic, electrochemical, electrical, and magnetic properties, or some combination thereof. In some embodiments, the support structure is a component covering some or all of the joint (e.g., a plastic, a metal, or any other suitable material). Alternatively, the support structure may be a semi-rigid strap wrapped around the joint. In some embodiments, the rigid properties of the support structure may be activated by an electrical signal indicating that the joint is moving beyond an acceptable or safe range of motion. The electrical signal may cause the support structure to adjust its position, shape, geometry, or pliability to prevent the joint from moving beyond the acceptable range of motion. In such embodiments, the support structure may include an electroactive gel or ferrofluid, which responds to the activation signal to adjust the properties of the support structure. Electroactive gels and ferrofluids are further discussed below with reference to FIG. 5.

To evaluate the performance and functionality of the joint covered by the plyowrap, the plyowrap is integrated with an array of sensors configured to measure and monitor the movement of the joint while the user wears the plyowrap. In some embodiments, the array of sensors characterizes the movement of the joint within the plyowrap based on how the plyowrap stretches or deforms due to the movement of the joint. FIG. 2 is an illustration of a knee wrap that includes a combination of electronic components that detect and analyze movements of the knee, according to one example embodiment. In the illustrated embodiment, the knee wrap 200 includes a tension sensor array 210, a controller 220, one or more sensors 230 (e.g., 230), and a battery 240 (or other power source). The knee wrap, for example the knee wrap 200, may also include a communication circuit (or chip) (e.g., WiFi or Bluetooth). The communication circuit 220 may be integrated with or communicatively coupled with the controller 220.

The tension sensor array 210 monitors the movements of the knee within the knee wrap 200. As illustrated in FIG. 2, tension sensors of the array 210 are arranged in a grid pattern along vertical and horizontal axes of the plyowrap. In such embodiments, the vertical and horizontal tension sensors create a map around the knee in any suitable coordinate system or mapping type, which may be used to monitor displacement, angular movements, and rotation of the knee. The map of sensors relays information about changes in the polygon shape of the knee during movement of the knee, which may be converted into a model of the kinematics of knee using machine-learning techniques or any other suitable computational technique.

In the illustrated embodiment, the array 210 covers only the knee of the user. In one embodiment, each tension sensor of the array 210 is a sensor wire, or thread, or a stretchable, tension meter. When the user moves their knee, the movement of the knee causes the tension sensors of the array 210 to stretch and generate signals describing the elastic movement of each tension sensor. Accordingly, the tension sensor array 210 may be used to measure properties of the joint including, but not limited to, displacement, elasticity, fatigue strength, lack of support, swelling, forces or pressure across the surface of the joint, the strength of dynamic stabilizers of the joint, acceleration and velocity of movements of the joint, and patterns of mobility based on joint and gait kinematics detected as the joint moves in real-time.

Additionally, tension sensors of the tension sensor array 210 are attached to the sleeve in such a manner that movement of the knee does not displace or dislocate individual tension sensors from their attachment sites in the array 210. The elastic properties of the pliable wrap and sensors of the array 210 allow both components to return to their initial geometry after being stretched or undergoing a deformation. Accordingly, the tension sensor array 210 may continuously track displacements and deformations caused by the movement of the knee over time. The tension sensor array 210 is further discussed below with reference to FIGS. 3A-C.

The controller 220, which is an embodiment of the controller 170, is a type of computer processing unit. The controller 220 receives signals from the tension sensor array 210 describing the movement of the tension sensors and extrapolates the movement of the knee from the elastic movements of the tension sensors. The controller 220 may be any suitable circuit board, integrated circuit chip or communication panel configured to process and transmit signals encoded with measurements recorded by the tension sensor array 210. For example, the contraction of a thigh during physical activity may cause a particular group of tension sensors of the array 210 to stretch or contract in response to changes in the geometry or shape of the quadriceps muscle resulting from the contraction. Measurements recorded by an elastic sensor of the array 210 (e.g., voltage measurement recorded by voltage sensors or tension measurements recorded by tension sensors) may be calibrated based on the distance that the sensor was stretched, for example due to flexion or vagus or varus tilt. Sensors of the sensor array 210 may be calibrated during the design of the knee wrap or at any point before a user begins using or wearing the knee wrap.

Other sensors of the array 210 or sensors embedded elsewhere in the knee wrap 200, (e.g., accelerometers and positioning sensors) may measure velocity, direction, location, or any other relevant characteristic of the movement of the knee. The movement of the knee or a representation of the movement of the knee may be extrapolated from measurements recorded by the sensor array 210 using machine-learned models, algorithms, or any other suitable computational tool. In some embodiments, the controller 220 compares data measured by the array 210 for a particular user with a normalized dataset and patterns of motion to compare the performance of the user's joint with varying segments and demographics of populations or individuals.

The controller 220 may be embedded into the knee wrap 200 or integrated in a removable manner. In some embodiments (not shown) the controller 220 is contained in a housing structure, which may be embedded or removably integrated into the knee wrap 200 to improve the durability of the controller 220 and prevent any damage to the controller caused by any element internal or external to the wearable brace.

A battery 240 is additionally embedded into the knee wrap 200 to power other electrical components of the wrap 200, for example the sensor array 210, the controller 220, and the sensors 230. The battery 240 may be a rechargeable battery or a disposable battery. The battery 240 may be fixed within the wrap 200 and have a recharging port. Alternately, the battery may be removable from the wrap 200 such that a user or provider may replace the battery 240 and connect the electrical components to a new battery. In some embodiments, the battery 240 itself and/or the connections between other electrical components and the battery are waterproof.

In the illustrated embodiment of FIG. 2, the knee wrap 200 further includes five sensors 230 in this example—sensors 230 a, 230 b, 230 c, 230 d, and 230 e—which are embedded into the wrap 200. Whereas measurements by the tension sensor array 210 characterize the movement of the knee within the knee wrap 200, the sensors 230 measure physiological properties of the body around the knee and the knee itself. Physiological sensors are further discussed below with reference to FIG. 5.

FIG. 3A is an illustration of an array of tension sensors (e.g., pressure sensor matrix) used to detect movements of the knee, according to one example embodiment. The tension sensor array 300 includes one or more vertical tension sensors 310, one or more horizontal tension sensors 320, and connection nodes 330 where a vertical tension sensor 310 intersects with a horizontal tension sensor 230. Each end of the vertical tension sensors 310 and the horizontal tension sensors 320 connect with the controller 220 (or other processing unit).

The tension sensor array 300 detects movements by the knee in any direction as a shifts in the geometry of the sleeve. As the knee moves within the plyowrap, the geometry of the plyowrap also changes given the pliability of the plyowrap. The tension sensor array 300 detects such changes in the geometry of the plyowrap when one or more tension sensors of the array 300 are stretched or compressed with the changing geometry of the plyowrap. Accordingly, signals transmitted by the tension sensor array 300 covering the knee may be interpreted as characterizing movements of the knee within the plyowrap.

As discussed above, tension sensors in the array 300 may be arranged in a grid pattern that spans over the knee. In the illustrated embodiment of FIG. 3A, tension sensors of the array 300 are oriented across both vertical and horizontal axes of the plyowrap such that vertically oriented tension sensor intersects with horizontally oriented sensor. As described herein, the intersection between a vertical tension sensor 310 and a horizontal tension sensor 320 is referred to as a “connection node.” As described herein, a connection node is a region of the sleeve with a concentration of circuits or sensors. The concentration of circuits or sensors at a connection node create a matrix of force measurements recorded over a large surface area, which may be used by the controller 220 or the brace feedback system 120 to generate a model of knee displacement and evaluate the movement of the knee based on the pattern of force displacement measured across connection nodes.

In the illustrated embodiment, sensors of the array 300 intersect at connection nodes 330 in a manner that conducts an electromagnetic field or transmits collective signals to a controller. Accordingly, connection nodes 330 represent regions of electrical connectivity between conductive components of the plyowrap. Connection nodes 330 may act as electrical conduits or transmit signals to a controller, for example the controller 170, for processing. For example, a signal carried by a conductive thread may describe the condition of a joint on multiple sides of the joint. The signal transmitted through connection nodes may include instructions for other parts of the knee wrap to react to the current condition of the joint, for example instructions for parts of the plyowrap to become more pliable, for parts of the plyowrap to become firmer, or both. In addition, or as an alternative, signals transmitted by a connection node may describe the concentration of forces, the movement of voltages. In other embodiments, sensors 310 may detect an overall electromagnetic field created by movement or stasis in the form and function of the wrap 350.

In alternate embodiments, the array of tension sensors may be arranged according to a different geometry, for example an alternatively shaped or angled pattern, such that vertical tension sensors 310 and horizontal tension sensors 320 overlap and intersect in a manner other than that illustrated in FIG. 3A.

In alternate embodiments (not shown), tension sensors of the array 210 may be replaced with stretchable voltage sensitive bands, conductive fibers woven into the material of the pliable wrap, or any other suitable elastic sensor. In embodiments where a wrap is embedded with voltage sensitive bands, a change in geometry of a muscle such as a quadriceps muscle during a thigh contraction may be characterized by a voltage change.

In some embodiments, tension sensors of the tension sensor array 310 are anchored in place by circuit bands lining the knee wrap. Each end of a tension sensor may be anchored independently by a circuit band embedded into a wrap. Alternatively, each end of the tension sensor may be integrated into a circuit contained in the circuit band. In some embodiments, circuit bands, such as the lateral bands 360, are circuits into which sensors of the tension sensor array 300 are integrated. In alternate embodiments, the circuit bands are designed from plyoplastic materials or fabrics to transmit sensor data, current, voltage, or any other relevant signal.

FIG. 3B is an illustration of a knee wrap 350 with an array of the tension sensors anchored by a combination of circuit bands integrated into the knee wrap, according to one example embodiment. The knee wrap 350 includes one or more lateral bands 360 (e.g., 360 a-b), a superior band 370, and an inferior band. The lateral band 360 anchors one end of the horizontal sensors of the tension sensor array 300. As illustrated in FIG. 3B, the lateral band 360 spans the vertical length of the wrap 350. In embodiments where each horizontal tension sensors lines the entire circumference of the wrap 350, both ends of each horizontal tension sensors are attached to a single lateral band. In other embodiments (not shown) the knee wrap 350 may additionally include a second lateral band 360 positioned on the opposite side of the wrap 350 to anchor the opposite end of the tension sensors. In additional to the linear bands illustrated in FIG. 3B, circuit bands may be designed in any other suitable geometry or shape.

The superior band 370 and the inferior band 380 anchor vertical tension sensors of the tension sensor array 300. As illustrated in FIG. 3B, the superior band 370 and the inferior band 380 each span a horizontal length of the wrap 350. The superior band 370 is embedded into a wrap such that the superior band anchors the upper end of vertical tension sensors (e.g., the end of a vertical tension sensor positioned above the joint covered by the wrap 350). In comparison, the inferior band 380 is embedded into a wrap such that the inferior band anchors the lower end of the vertical tension sensors (e.g., the end of the vertical tension sensors positioned below the joint covered by the wrap). The inferior band may be structurally and functionally similar to the superior band. In some embodiments, the tension sensors are only anchored by a superior lateral band positioned above knee. In some embodiments, the superior band 370 and the inferior band 380 themselves contain one or more tension sensors, voltage sensors, stretch sensors, or any other suitable elastic sensor, which allow the bands 370 and 380 to characterize movements of the part of the body covered by each band.

Compared to the embodiment illustrated in FIG. 2, the knee wrap 350 example in FIG. 3B includes a tension sensor array 310 that spans the full length of the wrap 350. Accordingly, the tension sensor array 310 may be used to measure the movement of the knee within the brace as well as movements of other body parts covered by the wrap 350, for example the lower thigh and upper shin.

FIG. 3C is an illustration of a knee wrap worn by a user bending their knee, according to one example embodiment. Continuing from the description above of the knee wrap 350, FIG. 3C illustrates a lateral view of the pliable knee wrap 350. Depending on the arrangement of tension sensors in the array 310, the number of tension sensors intersecting at a connection node my vary depending on the position of the connection node on the wrap 350. For example, the number of tension sensors intersecting at the connection node 390 is greater than other connection nodes of the knee wrap 350. Accordingly, the connection node 390 may also be referred to as a “high density node.” The concentration of sensors and circuits at a node, which characterizes a node as high density or low density, depends on the amount of information collected from sensors covering the portion of the knee at the node. For example, understanding rotational and patellofemoral motion requires more detailed sensor data, for example voltage sensor conductivity, than understanding extension movement in a single plane. Accordingly, a high density node, for example the connection 390, may be located at a position on the knee where such rotational and patellofemoral motion can be measured. A high density node may also be identified based on regions of the tension sensor array that are expected to experience significant conductive activity to change the shape of the wrap 350 or to transmit a large volume of signals. For example, a knee wrap includes a high density node on the lateral side to stiffen the lateral side. As another example, depending on the design considerations such as a lack of ACL support or posterolateral support, the knee wrap may have a low density node on the back of the knee and a high density a node laterally, medially, or around the patella.

Additionally, as discussed above, each circuit band anchoring the tension sensor array 310 (e.g., the lateral band 360, the superior band 370, and the inferior band 380) includes wiring or circuitry that connects tension sensors of the array 300 such that signals recorded by particular tension sensors may be transmitted to other tension sensors. Accordingly, a connection node located on a circuit band, such as the connection node 395, may be referred to as a primary circuit node. In some embodiments, the primary node represents a region of the tension sensor array 319 expected to experience the most significant conductive activity. The knee wrap 350 may further include secondary nodes (not shown), which are connection nodes that contain a secondary microprocessor or chip.

Movement of the knee may cause the geometry of the knee to dynamically change within the knee wrap. Accordingly, to maintain the form fitted design of the knee wrap, the knee wrap is designed to adjust its shape in real-time or near real-time to conform to the changing geometry of the knee. In some embodiments, the knee wrap includes one or more fluid chambers filled with an electroactive gel. Electroactive gels may be activated to stiffen particular portions of the brace, for example to dynamically improve joint support or to limit extension or biomechanical compromise of the joint. As described herein, fluid chambers are hollow pockets of varying shapes embedded into a wrap. A conductive wire sensor or thread may be attached to a fluid chambers to activate electroactive gels in the fluid chamber (or the fluid chamber itself) in response to signals from the controller to activate the electroactive gel or to dispense fluid in the chamber.

As discussed above, the wearable brace may be a plyowrap worn around a joint or body part other than the knee. FIGS. 4A-B illustrate an embodiment of a plyowrap worn around an ankle of the user, hereafter referred to as an “ankle wrap.” FIG. 4A illustrates a side view of an ankle wrap 405 according to one embodiment. The illustrated ankle wrap 405 includes two circuit bands 405 a and 405 b where tension sensors of a tension sensor array are integrated into the circuit bands. The circuit bands 405 a and 405 b functionally consistent with the circuit bands described above with reference to FIGS. 3A-C. In the illustrated embodiment of FIG. 4A, the circuit bands 405 a and 405 b are arranged to intersect across the ankle wrap. FIG. 4B illustrates a front view of an ankle wrap 405, according to one embodiment. The front view of the ankle wrap 405 additionally includes a circuit band 405 c, which anchors tension sensors of the array 210 across the foot of the patient. Although not shown, any suitable circuit board communication panel or housing may be integrated, embedded, or removably attached to the wrap 405 consistent with the above description.

In some embodiments, the plyowrap (e.g., the ankle wrap 405) fits over the foot while exposing the toes of the users. In particular embodiments, the ankle wrap 405 may include individual slits through which a user can fit each toe to expose the toes. In some embodiments, the plyowrap may cover part of the toes of the user, for example the metatarsals. In alternate embodiments, the ankle wrap 405 covers the entire foot of the user including their toes.

In some embodiments, a user may wear multiple plyowraps over different parts of the body, for example a knee wrap and an ankle wrap. In such embodiments, the multiple plyowraps may be connected by circuit bands with embedded tension sensors such that the movement of one joint (i.e., the ankle) may provide additional data or information for operating or adjusting the plyowrap around a second joint (i.e., the knee). Additionally, movement data collected by the part of the body between the two joints, over which the circuit bands traverse, may be encoded into signals for adjusting both the plyowrap around the first joint and the plyowrap around the second joint. FIG. 4C illustrates an interconnected knee wrap and ankle wrap 410, according to one embodiment. In the illustrated embodiment, a knee wrap 411 is connected to an ankle wrap 412 by intersecting circuit bands 413 a and 413 b.

In alternate embodiments, multiple joints may be covered by a single, uniform plyowrap, for example the knee and the ankle may be covered by a uniform plyowrap extending seamlessly over the leg of the user. In such embodiments, the circuit bands extend along the length of the plyowrap from the first joint of the user to the second joint of the user. FIG. 4D illustrates a plyowrap 415 that extends from the knee of a user to the ankle of the user, according to one embodiment. In the illustrated seamless knee and ankle wrap 415, two circuit bands 416 a and 416 b extend from the knee of a user wearing the wrap to an ankle of the user.

In alternate embodiments (not shown), the two plyowraps may be connected at a seam. As described herein, a seam between two plyowraps is a connection between the two plyowraps. In addition to connecting the material of the two plyowraps, the seam may additionally connect sensors, circuit bands, and other electronic components of the two plyowraps. A connection at a seam may be an adhesive connection (e.g., an adhesive material or a Velcro), an attachment mechanism (e.g., a buckle or a latch), or any other suitable means for coupling two wraps and any components of the two wraps together. In some embodiments, the connection at a seam may be configured to transmit digital information from a controller coupled to one plyowrap to a controller coupled to another plyowrap.

FIG. 4E illustrates an embodiment of a plyowrap worn over a hip joint or pelvis of a user, according to an embodiment. In the illustrated embodiment of FIG. 4E, the plyowrap resembles a pair of briefs, that only cover a portion of the user's legs. In alternate embodiments (not shown), the plyowrap may extend the length of the user's legs such that the plyowrap uniformly covers the hip, knee, and ankle joints of a user. Although not shown, circuit bands are flexible and may be integrated with a suitable circuit board communication panel or housing. The integration may be an embedded configuration or may be removably attached to the hip wrap 420 (e.g., consistent with the above description and/or in circuit module that may be inserted into a pocket and electronic connector to the circuit bands within the brace). In some embodiments, the hip wrap 420 may be designed using different materials for different portions of the wrap. Additionally, different areas of the plyowrap, for example the left and right side, may be independently activated or controlled such that the right side of the wrap 420 may adjusted to support the right hip independent of the performance of the left hip and vice versa.

FIG. 4F illustrates an embodiment of a plyowrap worn over a shoulder of a user, according to an embodiment. In alternate embodiments (not shown), the plyowrap may uniformly cover both shoulder of a user. Although not shown, circuit bands and any suitable circuit board communication panel or housing may be integrated, embedded, or removably attached to the wrap 425 consistent with the above description. For example, the circuit backs may be structured with the garment along the radius of the arm, shoulder and or torso area. In some embodiments, the shoulder wrap 425 may be designed using different materials for different portions of the wrap. Additionally, different areas of the plyowrap, for example the left and right side, may be independently activated or controlled such that the right side of the wrap 425 may adjusted to support the right shoulder independent of the performance of the left shoulder and vice versa. The sleeve of the shoulder wrap 420 may be further configured to securely fit over the should of a user using any material suitable for preventing the material from slipping while being worn by the user.

FIG. 4G illustrates an embodiment of a plyowrap worn over an elbow of a user, according to an example embodiment. Although not shown, circuit bands and any suitable circuit board communication panel or housing may be integrated, embedded, or removably attached to the wrap 430 consistent with the above description (e.g., circuit band along the circumference of the sleeve). In some embodiments, the arrangement of circuit bands and the tension sensor array in the elbow wrap 430 may be consistent with the arrangements discussed in FIGS. 3A-C. Alternatively, FIG. 4H illustrates an embodiment of an elbow wrap where circuit bands are connected at a primary node positioned on the elbow wrap 430 over the elbow of a user, according to one embodiment. In the illustrated embodiment of FIG. 4H, circuit bands 431 a, 431 b, 431 c, and 431 d are interconnected at a primary node 432 positioned over the elbow. In some embodiments (not shown), the plyowrap may extend over the wrist of a user, with slots for the user to insert their fingers to secure the position of the plyowrap wrap.

Consistent with the description above of the knee wrap 350 and the ankle wrap 405, a shoulder wrap 425 and an elbow wrap 430 may be connected at a seam or seamlessly integrated into a single uniform plyowrap. Similarly, an elbow wrap 430 and a wrist wrap 435 may be connected at a seam or seamless integrated into a single, uniform plyowrap.

FIG. 4I illustrates a wrist wrap 435, according to one embodiment. The illustrates wrist wrap 435 includes several intersecting circuit bands 436, which form a tension sensor array 210. The circuit bands 436 are functionally consistent with the circuit bands described above with reference to FIGS. 3A-C. Although not shown, any suitable circuit board communication panel or housing may be integrated, embedded, or removably attached to the wrist wrap 435 consistent with the above description. In some embodiments, the wrist wrap 435 fits over the hand with slits that expose the fingers of a user. In other embodiments, the wrist wrap 435 fits over the hand like a glove including the fingers of the user.

Returning to the embodiment where the sleeve is worn around a knee, FIG. 5 is an illustration of a knee wrap 500 with electroactive fluid chambers, according to one example embodiment. In the illustrated embodiment of FIG. 5, one or more fluid chambers 510 and a tension sensor array 520 are embedded in the knee wrap 500. The tension sensor array 520 may be structured as previously described, e.g., tension sensor array 210, 300. The fluid chambers 510 may be located at, or in proximity to, each connection node created of the tension sensor array 520. For example, the fluid chambers 510 may be located at the intersection of the vertically oriented tension sensors, e.g., 310, and the horizontally oriented tension sensors, e.g., 320. Alternately, the fluid chambers 510 may be located with the boundaries between parallel vertically oriented tension sensors, e.g., 310 and parallel horizontally oriented tension sensors, e.g., 320, where the vertically oriented tension sensors, e.g., 310, is adjacent to the horizontally oriented tension sensors, e.g., 330.

The number and location of fluid chambers 510 may be adjusted based on the joint covered by the wrap 500, the relative function and performance of the joint, or any other relevant factors. Further, the fluid chambers 510 may be self-contained or may be interconnected with each other within a closed system where fluid can flow at different rates and levels between them to increase or decrease fluid density within each fluid chamber 510.

The electroactive gel within a fluid chamber 510 may be activated by an electrical signal or any other suitable electric stimuli. Upon activation, the electroactive gel undergoes a reaction that changes its physical properties, adjusting the pliability of the knee wrap itself by stiffening or softening the wrap 500 to accommodate various clinical scenarios. For example, activation of the electroactive gel in a fluid chamber 510 may cause the gel to transition into a semi-solid or solid state, reducing the pliability of the portion of the knee wrap surrounding the fluid chamber 510. In alternate embodiments, the fluid chambers 510 are filled with ferrofluid for dynamically adjusting of the geometry, orientation, location, or position of the wrap 500.

Accordingly, a controller embedded in the wrap 500, for example the controller 220, characterizes the movement of the knee based on signals received from the tension sensor array 210 and communicate electronic signals to one or more fluid chambers 510 to trigger the property-changing reaction in the electroactive gel within the fluid chambers 510. The controller 220 may apply the measurements recorded by the tension sensor array 520 to a machine learned model trained to identify a possible biomechanical compromise of the joint and the risk of the compromise occurring. The model may be trained using a training dataset collected from a population of patients, where the data is labeled with the compromise experienced and the tension measurements that correlated with the compromise. A second machine learned model, or alternatively the same model, may be used to identify adjustments to the pliability of the wrap 500, if any, that would limit the biomechanical compromise to the joint identified by the first model. The controller 220 may transmit signals for activating electroactive gel in one or more fluid chambers to change the shape or geometry of the knee wrap 500 according to the adjustments determined by the second model.

In some embodiments, a conductive thread connects each tension sensor of the array 520 to the controller 220 and connects the controller 220 to each of the fluid chambers. The conductive thread transmits tension changes from one tension sensor of the array 210 to another or to nodes where multiple tension sensors intersect. Based on measurements recorded by multiple sensors of the array 210, the controller 220 may generate instructions for activating electroactive gels in one or more fluid chambers to adjust the shape or geometry of the wrap 200 and transmit a current via the conductive thread to one or more fluid chambers to active the electroactive gel. In alternate embodiments, the electroactive gel may be activated by a voltage signal or an electromagnetic field. The amplitude of the current or voltage or the shape of an electromagnetic field generated in proximity to a fluid chamber causes electroactive gel in the fluid chamber to stiffen or soften, changing the geometry and structure of the knee wrap 200. For example, a valgus load applied to the knee places the medial collateral ligament (MCL) at risk of tearing. To reduce the risk of biomechanical compromise of the MCL or body parts surrounding the MCL, the conductive thread transmits an electrical current to activate electroactive gels in fluid chambers on the medial side of the knee, causing the wrap 200 to stiffen and support the MCL in its natural physiologic state.

In some embodiments, a conductive thread connects fluid chambers 510 in a manner that enables the serial activation of the gels in the chambers. In such embodiments, electroactive gels within individual fluid chambers may be activated separately and independently at different times and in varying orders depending on the types of electroactive gels within the chambers, the position of the fluid chambers relative to the knee, or a combination thereof. Accordingly, the controller 220 may transmit signals to manage, activate, and deactivate electroactive gels in particular fluid chambers at different times or in different patterns.

In an alternate embodiment (not shown), the knee wrap is embedded with one or more strips filled with electroactive gel, also referred to as “stays.” Each stay extends the length of the knee wrap from the superior circuit band to the inferior circuit band. The stays may be arranged in various geometries, shapes, and orientations depending on the joint covered by the plyowrap, the size of the plyowrap, the functionality and performance of the plyowrap, or any other relevant characteristics.

In addition to adjusting the geometry and pliability of the plyowrap based on the movement of the knee, a wearable brace 110 may also monitor and manage physiological properties of the user in real-time or near-real time. As discussed above, in addition to the tension sensor array, the knee wrap is embedded with a combination of sensors configured to monitor various physiological parameters that describe in part, or together as a whole, the patient's current health and fitness.

FIG. 6 is an illustration of a knee wrap with sensors configured to monitor physiological parameters and detect changes in those parameters, according to one example embodiment. In the illustrated embodiment, the knee wrap 600 is embedded with a combination of physiological sensors 610, nutrition chambers 620, and external chambers 640.

Physiological sensors 510 measure and detect changes in physiological properties such as in the temperature, O₂ saturation, glucose, energy, heat, elasticity, vibration, blood pressure, heart rate, conductivity, sensitivity, ultrasonic impedance, electromagnetic frequency, chemistry, sweat analytes, myoelectric feedback, optical visualization, or any other suitable physical or physiological properties. In particular, electromagnetic physiological sensors may be used to detect changes in the electromagnetic current in the sleeve during static and movement activities of the body. Thermal physiological sensors may be used to detect temperature changes in the body based on measurements of the temperature of the skin surface. Ultrasonic sensors may be used to detect sound waves across the body using ultrasound sensors. Electromyograph sensors may be used to measure electrical signals generated by muscles during the movement of the joint. Based on measurements recorded by the physiological sensors 610, the controller 220 generates and transmits signals (e.g., an electric current) that cause nutrition chambers 620 or any other chambers to deliver nutrients or any other suitable substance to the user.

In some embodiments, nutrition sensors (not shown) are embedded into the knee wrap 600 to detect changes in nutrient levels in the body, for example vitamins, minerals, glucose, proteins, carbohydrates and electrolytes. The nutrients as described may be substrates not subject to regulation, e.g., by the Food and Drug Administration (FDA), which can improve the health of an individual. For example, nutrition sensors may identify changes in the user's chemical balance based on sweat analysis to determine the user's nutritional needs. Based on the user's nutritional needs, the knee wrap 600 may deliver a nutrient supplement or transmit a notification to a patient device 130 with instructions for the user to take a nutritional supplement. Alternately, the nutrition sensors may be used to transmit regulatory approved substances, e.g., medical use substances.

The nutrition chambers 620 are fluid chambers containing a semi-solid or liquid substrate mixture of the nutrients, vitamins, and minerals that can be delivered to the joint or body parts surrounding the joint. The interior wall of nutrition chambers 620, which is positioned within in contact with the skin of the user may be permeable or semi-permeable to allow for the transdermal delivery of one or more of the nutrients contained in the fluid chamber. Described differently, the interior wall of the nutrient chamber 620 faces the skin of the user when the wrap 600 covers the joint. The interior wall of the nutrient chamber 620 may allow the nutrients within the chamber to diffuse onto the skin at a steady rate. For example, the controller 220 may determine that a user's sodium levels are decreasing at a steady rate based on measurements by the physiology sensors. Accordingly, the interior wall of the nutrient chamber 620 may allow for the steady transdermal delivery of sodium over an extended period of time.

In other embodiments, the permeability of the interior wall may be regulated in response to a signal from the controller 220 based on feedback from a physiological sensor or in response to a user-activated electronic signal. For example, a physiological sensor detects sweats analytes on the skin of a user indicating the user has low sodium levels. In response, the controller 220, or alternatively a connection node of the tension sensor array, transmits a signal (e.g., a current or voltage) that alters the permeability of the transdermal surface of one or more nutrient chambers to deliver nutrients to restore the sodium levels. In some embodiments, the transmitted signal directly transforms the transdermal surface of the nutrition chambers. The change in permeability may be maintained for a period of time as determined by the user or until the controller 220 receives feedback from the physiology sensor confirming restored nutrient levels. Alternatively, the period of time may be determined using a model, or any other suitable computational technique, trained to determine the amount of time for levels of a particular nutrient to rise.

In alternate embodiments, nutrients stored within the nutrition chamber 620 are delivered by a transcutaneous delivery pump. Such a delivery pump may be activated electronically by a signal from the controller generated in response to a measurement by physiological sensors 610. Alternatively, the delivery pump may be activated manually, for example using an activation switch, a pump, a button on the microprocessor, or via a remote signals from a mobile application on a patient device 130 or a provider device 140. For example, if a user running with the knee wrap 600 recognizes that they are sweating profusely, they may manually activate the delivery pump of a nutrition chamber 620 containing an electrolyte mixture to deliver the mixture. In embodiments involving a delivery pump, the nutrient is applied to the skin of a user in doses in response to a signal from the controller. Returning to the example involving the nutrition chamber 620 containing sodium, when physiological sensors 610 measure a sodium level below a threshold, the controller may generate and transmit a signal triggering the transdermal or transcutaneous delivery of the nutrients in the chamber 620 in real-time.

In some embodiments, the knee wrap 600 may include additional chambers functionally and structurally similar to the nutrition chambers 620. Such additional chambers may include substances, liquids, gels, or a combination thereof that may be applied to the user's skin, may diffuse through the user's skin, or both. For example, if a user is diabetic, the physiological sensors 810 detect low levels of transcutaneous glucose which cause the controller 610 to generate and transmit a signal for additional fluid chambers to apply glucose transdermally or transcutaneously, for example using a delivery pump. As another example, such additional fluid chambers may contain a pain medication, for example a numbing cream, which is dispensed in response to user feedback or physiological measurements indicating that the user is in pain. In embodiments where changes in the Cartesian or non-Cartesian grid created by the tension sensor array 610 indicate a pattern of motion indicative of a biomechanical compromise to the joint (e.g., a knee dislocation), the controller 220 may automatically trigger chambers to administer a liquid or gel onto the skin of the patient or activate electroactive fluids to immobilize the knee like a splint.

The techniques discussed above with regards to transdermal and transcutaneous delivery of nutrients may also be applied here for the transdermal and transcutaneous delivery of any other suitable substance, liquid, or gel.

Nutrition chambers 620 are structurally consistent with the fluid chambers filled with electroactive fluids described above with regards to FIG. 5. Additionally, nutrition chambers 620 may include mechanical components that allow a user to refill the chambers during use or between uses of the knee wrap 600. In one embodiment, the chambers 620 and 630 include a value where a user can add nutrients to the chamber. In alternate embodiments, the chambers 620 and 630 may include an injection port where a user can fill the chamber with nutrients.

In some embodiments, contents of nutrition chambers 620 and any other fluid chambers may be applied in real-time or near real-time in response to measurements by the physiological sensors 610. Such real-time or near real-time responses allow the knee wrap 600 to accommodate or adjust to changes in the physiological state of the joint covered by the knee wrap 600 while in use or in motion. For example, a user running a marathon may get dehydrated in the 12^(th) mile, causing their potassium levels to drop. Physiology sensors 610 may detect the lowered potassium levels based on sweat analytes or the skin surface. When the potassium levels drop below a threshold level, the controller 220 transmits a signal causing the electric activation of a pump to deliver potassium to the user. Alternatively, the controller 220 may transmit a notification or reminder to a patient device 130 instructing the user to manually activate the pump. The description above may be applied to any suitable vitamin, or nutrients, for example sodium, sugar, glucose, vitamin C, vitamin D, an electrolyte mixture, or some other combination of vitamins, and nutrients.

The knee wrap 600 further includes external chambers 640 coupled to the exterior of the knee wrap 600 using any suitable attachment technique or mechanism. In other cases, the chambers are integrated in the wrap during manufacturing without additional stitching/adhesion or attachment process. In some embodiments, the external chambers 640 contain solid vitamins, or other nutrients. The external chambers 640 may also store mixtures of vitamins or mixtures of nutrients. During use of the knee wrap 600, a user may access the external chambers 640 to consumer the contents of the chambers or to fill a chamber of the knee wrap. External chambers 640 may be designed in any suitable geometry.

FIG. 7A is an illustration of a cross-section of a knee wrap that includes an outer layer and an inner layer, according to one example embodiment. In the illustrated embodiment, the wrap 1000 covers the knee 1025 of a user. For the sake of description, the lateral side 705, the medial side 1010, the anterior side 715, and the posterior side 1020 are labeled in FIG. 7A. An external chamber 745, an embodiment of the external chambers 640, is coupled to the outer layer. Fluid chambers containing liquid or semi-solid substrates to be delivered to the user, for example the nutrient chambers 750, are embedded into the inner layer 740 such that the fluid chambers contact the skin of the joint and body parts surrounding the joint. Because the inner layer 740 and components embedded into the inner layer 740 contact the skin of the user, the inner layer 740 may be slip resistant or include a slip resistant lining on the surface contacting the skin.

In the illustrated embodiment of FIG. 7A, the tension sensor array 760, which is an embodiment of the tension sensor array 210, and the conductive thread 765 connecting the sensors of the array 760 are positioned between the outer layer 630 and the inner layer 740. In alternate embodiments, the tension sensor array 210 may be embedded into the material of outer layer 730 or the inner layer 740.

In the illustrated embodiment, the delivery pump 770, which facilitates transcutaneous delivery of nutrients, or vitamins, is embedded in both the outer layer 730 and the inner layer 740. The applying end 775 of the pump 770 extends through the inner layer 740 to contact the skin of the user. As described herein, the applying end 775 is the end of the pump 770 that delivers the contents of the pump. In the illustrated embodiment of FIG. 7A, the opposite end, the activating end 780 of the pump 770 extends through the outer layer 750 such that the user may access the activating end 780 to manually activate the pump to deliver its contents. In embodiments (not shown) where the pump 770 is electrically activated, the activating end may only extend into contact with the conducting thread 755 such that the controller 1020 may communicate electronic signals via the thread 755 to activate the pump.

FIG. 7B is an illustration of a cross-section of a knee wrap that includes an outer layer, an inner layer, and an alternate configuration of the tension sensor array, according to an example embodiment. Consistent with the description in FIG. 7A, the knee wrap 780 illustrated in FIG. 7B includes an outer layer 730 and an inner layer 740. Recalling the description above of the tension sensor array 210, tension sensors may be oriented along a vertical axis or along a horizontal axis of the wrap 780. Returning now to the embodiment illustrated in FIG. 7B, vertical tension sensors 785 are embedded into the outer layer 730 and horizontal tension sensors 790 are embedded between the outer layer 730 and the inner layer 740.

Additionally, in the embodiment illustrated in FIG. 7B, nutrition chambers, and fluid chambers containing electroactive gels are embedded between the inner layer 740 and the outer layer 730. In such embodiments, the inner layer 740 is permeable to facilitate the diffusion and delivery of contents within the nutrition chambers onto the skin of the user. The inner layer 740 is additionally pliable to facilitate changes in the geometry of the wrap 780 caused by the activation of the electroactive gels within fluid chambers.

In some embodiments, fluid chambers containing electroactive gels are embedded in each of the inner layer 740 and the outer layer 730. In such embodiments, the fluid chambers of the inner layer 740 may be connected to the controller by a separate conductive thread and control circuitry than the fluid chambers of the outer layer 730. Accordingly, a controller may generate signals with instructions for the inner layer 740 to change its structure or geometry differentially from the outer layer. For example, electroactive gels in fluid chambers of the outer layer 730 may be activated to stiffen the outer layer 730 to protect against a biomechanical compromise or impact of the joint, while electroactive gels in fluid chambers of the inner layer 740 may be activated to soften the inner layer 730 to dampen the force on the joint. The inner layer 740 may also be made from fabrics, materials, or layers of various fabrics to dampen forces on the joint.

IV. Brace Feedback System

As previously described, when a plyowrap is worn around a joint to measure neuromusculoskeletal function, the brace feedback system 120 analyzes movements of the joint based on changes in a contour digital map describing the movement and performance of the joint over time, measurements recorded by wired and wireless sensors, or a combination thereof. FIG. 8 is a block diagram of the system architecture of the brace feedback system 120, according to an example embodiment. The brace feedback system 120 illustrated in FIG. 8 includes a joint performance evaluator 810, a joint support module 820, and a joint therapy module 830. However, in other embodiments, the brace feedback system 120 may include different and/or additional components.

As described above, a tension sensor array (e.g., the tension sensor array 210) embedded into a plyowrap collects signals describing abnormalities in physiology or vital signs associated with the joint or muscle. The joint performance evaluator 810 may characterize motion of the joint (e.g., extension and displacement of the knee) relative to an expected plane of motion based on the collected sensor data describing physical forces, electromagnetic forces, and vibration activity. Consistent with the description above, for example, with regards to FIG. 1, the representation may be a numerical value. For example, this may include a displacement of the motion of the joint relative to an expected plane of motion of the joint, or a visual/graphic model of the motion of the joint.

In some embodiments, the joint performance evaluator 810 implements a machine learned model to receive, as inputs, signals collected by the tension sensor array 210 and to generate a representation of the motion of the joint. Accordingly, the joint performance evaluator may apply machine-learning techniques to model (or predict) a movement of the joint based on a combination of sensor data collected by the tension sensor array. As described herein, “a prediction” generated by joint performance evaluator 810 using a machine-learned model may also characterize a risk of a biomechanical compromise of the joint or an expectation that joint will experience a biomechanical compromise of the joint. The joint performance evaluator 810 may encode the collected sensor data into a feature vector, or any other format suitable to be processed by the model. To predict the movement of the joint, the model may be a mathematical function or other more complex logical structure, trained using a combination of historical sensor data collected for the joint to determine a set of parameter values stored in advance and used as part of the predictive analysis. As described herein, the term “model” refers to the result of the machine learning training process. Specifically, the asset classification model 350 describes the function for predicting the classification of an asset and the determined parameter values incorporated into the function. “Parameter values” describe the weight associated with at least one of the featured principal components.

A machine-learned model implemented by the joint performance evaluator 810 is trained using a training dataset made up of large volume of historical sensor data collected from joints of a population of users. In some embodiments, the machine-learned model may be trained to generate predictions for a particular joint, for example a knee. In such embodiments, the training dataset upon which the model is trained comprises historical sensor data collected for a population of users wearing a knee brace. In other embodiments, the machine-learned model may be a baseline model trained to generate predictions regarding the movement, function, or position of any joint. In such embodiments, the training dataset upon which the model is trained comprises historical sensor data collected for a population of users wearing any of the wearable braces described herein.

Each entry of the training dataset represents a record of measurements collected by the tension sensor array labeled with an identifier of the joint for which the data was collected and the known movement of the joint corresponding to the record of measurements. During training, the machine-learned model determines parameter values for each encoded feature (e.g., measurement collected by the tension sensor array) of the feature vector input to the model by analyzing and recognizing patterns and correlations between the collected measurements and the labeled movement or range of motion of the joint. As patterns and correlations are confirmed by providers, new sensor data is collected for existing users continuing to wear a wearable brace, and sensor data is collected new users beginning to wear a wearable brace, the training dataset on which the model is trained may be continuously updated with entries pertaining to the new sensor data. Accordingly, the machine-learned model may be iteratively trained based on the updated data in the training dataset to continuously improve the accuracy of predictions generated by the joint performance evaluator 810.

The joint performance evaluator 810 may compare the predicted motion of the joint to the expected motion of the joint to determine whether the prediction motion is within a threshold deviation of the expected motion. If the range of motion does not satisfy the threshold deviation (e.g., the knee is moving beyond the standard plane of motion of a human knee), joint performance evaluator 810 may generate a signal indicating the likelihood of a biomechanical compromise to the knee, that the knee should be further supported by the knee wrap, or both.

In some embodiments, the joint performance evaluator 810 may additionally categorize the user into a biomechanically modeled risk level for compromise of the joint (e.g., “high risk,” “medium risk,” “low risk”) or biomechanically modeled behavior of the joint.” In doing so, the joint performance evaluator 810 may compare the difference between the prediction motion of the joint (generated by the joint performance module 810) and the expected motion of the joint against multiple thresholds, where each threshold represents a category of a biomechanical risk level for compromise in the position or function of the joint. Described differently, the categorized risk level of a joint describes whether the joint satisfies a given threshold for a potential biomechanical compromise of the joint. Based on the category of biomechanical risk level assigned to the joint, the joint support module 820 may generate instructions for activating electroactive gels in varying amounts of fluid chambers 510, activating electroactive gels to form varying shapes or levels of stiffness, activating other components of the wearable brace, or a combination thereof to support the joint or mitigate the biomechanical risk level of the joint.

The signal may additionally, or alternatively, include instructions for dispensing supplements or for applying any suitable treatment to the knee. The generated signal may be transmitted to the joint support module 820 or a trainer, coach, or third-party monitoring the user's physical condition.

The joint support module 820 analyzes the movement of the joint predicted by the joint performance evaluator 810 to determine adjustment parameters for the plyowrap based on the risk of biomechanical compromise of the joint or the categorized biomechanical risk level assigned to the joint. The joint support module 820 determines adjustment parameters for the plyowrap based on the determined risk or the categorized risk level of the joint. As discussed herein, adjustment parameters refer to structural properties of the plyowrap, components of the plyowrap that may be adjusted to affect structural properties of the plyowrap (e.g., the fluid chambers 510), or a combination thereof. Accordingly, the joint support module 820 may determine adjustment parameters to increase propulsion, acceleration, reaction time, strength, resistance, elasticity, rigidity, impedance, and any other suitable action to adjust performance of a joint, muscle, part of the body, or body as a whole. For example, for a joint categorized into a biomechanical high risk level, the joint support module 820 may determine adjustment parameters for stiffening the entire plyowrap. In comparison, for a joint categorized into a medium risk level or a low risk level, the joint support module 820 may determine adjustment parameters for stiffening only a portion of the plyowrap to limit a certain degree of movement by the joint. In the latter embodiments, adjustment parameters may additionally identify a particular subset of fluid chambers, in which to activate electroactive gel to limit the movement of the joint.

Accordingly, the joint support module 820 generates instructions for adjusting structural properties of the plyowrap based on the determined adjustment parameters. The joint support module 820 transmits the generated instructions to a local controller coupled to the plyowrap, which may adjust the structural and physical properties of the plyowrap according to the adjustment parameters. In some embodiments, the instructions are encoded into a signal, which is transferred to a controller embedded into the plyowrap, for example the controller 220 of the knee wrap 200). The controller 220 executes the encoded instructions by transmitting signals electronic signals that stimulate electroactive gels in particular fluid chambers to stiffen in support of the joint or nutrition chambers to dispense nutrients at the joint. In some embodiments, the functionality discussed herein with reference to the joint support module 820 may be performed locally by a controller embedded into the plyowrap.

Accordingly, the joint support module 820 generates instructions for the controller 220 to stimulate electroactive gels in a fluid chamber to stiffen the plyowrap into a rigid splint, for example for transport, healthcare intervention, delivery of physical or medical therapy, or a combination thereof. The joint support module 820 may identify which fluid chambers to activate electroactive gels using an algorithm extracted from models of an optimally performing joint and models of an joint performing sub-optimally. The joint support module 820 may generate particular instructions to affect varying levels of rigidity in the plyowrap. For example, the joint support module 820 may generate instructions to define the rigidity of the plyowrap to keep a joint straight or with a customization to a particular parameter of the joint, for example a 30-degree bend at a joint.

In some embodiments, the sensor data collected by the tension sensor array may characterizes the acceleration of the joint. The joint performance evaluator 810 may predict, based on the collected sensor data, that the user is at risk of a biomechanical compromise to the joint that could cause the user to fall in the near future. In such embodiments, the joint support module 820 determines adjustment parameters to shift or decelerate movement of the joint to prevent the predicted biomechanical compromise and generate instructions for the controller 220 activate electroactive gel in particular fluid chambers accordingly.

In an embodiment where a user wears a knee wrap, the joint support module 820 may determine that the user is at (modeled biomechanical) risk of compromising their anterior cruciate ligament (ACL) based on an analysis of the movement of the user's knee modeled by the joint performance evaluator 810. In response, the joint support module 820 may determine adjustment parameters to limit the anterior tibial translation and rotation kinematics of the knee and generate and encode instructions for a controller coupled to the knee wrap to transmit electronic signals to activate electroactive gels in fluid chambers of the knee wrap accordingly.

In some embodiments, the joint support module 820 may activate electroactive gels in the fluid chambers of the plyowrap in response to an external stimulus or an external impact to the joint, for example a bullet making contact with the body. In addition to detecting the external stimulus, the tension sensor array may detect the velocity with which the stimulus made contact with the joint and extract additional intrinsic properties of the joint based on the contact. In response, the joint support module 820 may determine adjustment parameters to adjust physical properties of the plyowrap to support to area of the joint affected by the stimulus as well as to provide some amount of protection to t the joint against additional external stimuli without affecting the range of motion of the joint. Continuing from the example above regarding the bullet wound, the joint support module 820 may transmit instructions to activate electroactive gels to stiffen such that areas of plyowrap become bulletproof or are able to withstand bullets.

In addition to generating instructions to support a joint by adjusting the rigidity or physical properties of a plyowrap, the brace feedback system 120 may enable various therapeutic modalities. Such therapeutic modalities may also be controlled manually by the user or a health care provider. In one embodiment, the wearable brace may store a cooling material in a chamber of the plyowrap or within the plyowrap itself. Accordingly, the joint therapy module 830 may generate instructions for the controller 220 to execute a cooling therapy. The joint therapy module 830 may generate instructions for the controller to pump the cooling material through conduit channels integrated into the plyowrap of the plyowrap. Alternatively, in embodiments where the electroactive gel stored in fluid chambers has cooling properties, the joint therapy module 830 may generate instructions for the controller to activate the electroactive gel in particular fluid chambers. In some embodiments, the functionality discussed herein with reference to the joint support module 820 may be performed locally by a controller embedded into the plyowrap.

In other embodiments, the plyowrap is configured with components, capable of transcutaneous application of a substances stored in fluid chambers of the plyowrap, for example a delivery pump. Examples of such substances include, but are not limited to, therapeutic gels, vitamins, electrolytes, molecular nutrients, or any other suitable supplement. In a particular embodiment, sensors in the plyowrap may detect that the glucose levels of a diabetic runner are higher than the “normal level.” In response, the joint therapy module 830 may generate and transmit instruction to the controller 830 to actuate a component of the plyowrap to administer a substance (stored in a fluid chamber of the plyowrap) to the runner to avoid ketoacidosis. In another embodiment, the joint therapy module 830 may generate and transmit a notification or suggestion to marathon runner to manually take electrolyte gel capsules based on changes in the runner's body chemistry detected by sensors in the wearable brace.

The joint therapy module 830 may additionally generate instructions for adjusting the rigidity and structural properties of the plyowrap to aid in a user's physical therapy. For example, the joint therapy module 830 may instruct the controller 220 assist in resistance training of the joint by limiting the force applied through the joint during rehabilitation or post-surgery recovery. Accordingly, the plyowrap may be incorporated into rehabilitation of the joint by enabling passive motion of the joint, active motion of the joint, and resistance. In some embodiments, the joint therapy module 830 may receive instructions from a health care provider and transmit those instructions to the local controller embedded into the plyowrap for execution.

In some embodiments where the plyowrap 110 is integrated with vibrational sensors or other tactile sensors. In such embodiments, the joint therapy module 830 may generate instructions for the local controller to activate such sensors to displace and shift the geometry and force of the plyowrap 110 to provide a massage based on feedback from sensors on the impedance, vibration, and elasticity of the joint and muscle covered by the wearable brace.

In embodiments where the plyowrap is a chest sleeve, the wearable brace 110 may additionally monitor electrocardiogram changes and provide automated defibrillation. Accordingly, the joint therapy module 830 may generate instructions for defibrillating a user in response to a signal detecting changes in electrocardiogram measurements.

FIG. 9 is an exemplary data flow for adjusting properties of the plyowrap to support a joint or muscle, according to an embodiment. The brace feedback system 120 receives 910 signals collected by the sensors embedded into a plyowrap worn around the joint of a user. Data collected by tension sensors may describe the movement of the joint, for example the range of motion of the joint and the plane in which the joint moves. Data collected by physiological sensors may describe the physiology or physiological condition of the joint. Based on the signals received from the plyowrap, the brace feedback system 120 generates 920 a prediction of the movement or position of the joint. The brace feedback system 120 may implement machine-learning techniques to generate a representation of the motion of the joint. The representation, also referred to as “a prediction,” may additionally categorize the user's biomechanically modeled risk of compromise of the joint or an expectation that a compromise of the joint will occur. In some embodiments, the brace feedback system 120 may predict the performance of the joint by implementing other suitable algorithmic techniques or by generating a visual model of the movement of the joint.

The brace feedback system 120 may determine whether the user is at risk of an biomechanical compromise of the joint based on the predicted performance of the joint. If the user is at risk, the brace feedback system may additionally categorize 930 the biomechanical risk level to the joint based on a difference between a predicted performance of the joint and an expected performance of the joint. The brace feedback system 120 may compare the predicted performance to an actual, or healthy, performance of the joint. If the predicted performance differs from the actual performance by at a least a threshold amount, the brace feedback system may determine that the user is at risk of a biomechanical compromise. The brace feedback system 120 determines 940 adjustment parameters for the brace based on the categorized risk level. The adjustment parameters, when executed, adjust structural properties of the plyowrap to improve performance of the joint. In some embodiments, the generated instructions cause a local controller coupled to the plyowrap to activate electroactive gel in fluid pockets of the plyowrap. The stimulated electroactive gel causes the plyowrap or portions of the plyowrap to stiffen or harden to support the movement of the joint, support the joint, or a combination thereof. Alternatively, or in addition to, the generated instructions stimulate electroactive gel to adjust other physical properties of the plyowrap to support optimal movement of the joint. The brace feedback system 120 transmits 950 instructions to a local controller on the plyowrap, which executed the instructions to adjust structural and physical properties of the plyowrap based on the determined adjustment parameters.

V. Computing Machine Architecture

FIG. 10 is a block diagram illustrating components of an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller), according to one embodiment. Specifically, FIG. 10 shows a diagrammatic representation of a machine in the example form of a computer system 100 within which program code (e.g., software) for causing the machine to perform any one or more of the methodologies discussed herein may be executed. The digital brace system 100 may include some or all of the components of the computer system 1000. The program code may be comprised of instructions 1024 executable by one or more processors 1002. In the tablet scribe device 110, the instructions may correspond to the functional components described in FIGS. 2-9.

While the embodiments described herein are in the context of the digital brace system 110, it is noted that the principles may apply to other touch sensitive devices. In those contexts, the machine of FIG. 10 may be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a smartphone, a web appliance, a network router, an internet of things (IoT) device, a switch or bridge, or any machine capable of executing instructions 1024 (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute instructions 1024 to perform any one or more of the methodologies discussed herein.

The example computer system 1000 includes one or more processors 1002 (e.g., a central processing unit (CPU), one or more graphics processing units (GPU), one or more digital signal processors (DSP), one or more application specific integrated circuits (ASICs), one or more radio-frequency integrated circuits (RFICs), or any combination of these), a main memory 1004, and a static memory 1006, which are configured to communicate with each other via a bus 1008. The computer system 1000 may further include visual display interface 1010. The visual interface may include a software driver that enables displaying user interfaces on a screen (or display). The visual interface may display user interfaces directly (e.g., on the screen) or indirectly on a surface, window, or the like (e.g., via a visual projection unit). For ease of discussion the visual interface may be described as a screen. The visual interface 1010 may include or may interface with a touch enabled screen. The computer system 1000 may also include alphanumeric input device 1012 (e.g., a keyboard or touch screen keyboard), a cursor control device 1014 (e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument), a storage unit 1016, a signal generation device 1018 (e.g., a speaker), and a network interface device 1020, which also are configured to communicate via the bus 1008.

The storage unit 1016 includes a machine-readable medium 1022 on which is stored instructions 1024 (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions 1024 (e.g., software) may also reside, completely or at least partially, within the main memory 1004 or within the processor 1002 (e.g., within a processor's cache memory) during execution thereof by the computer system 1000, the main memory 1004 and the processor 1002 also constituting machine-readable media. The instructions 1024 (e.g., software) may be transmitted or received over a network 1026 via the network interface device 1020.

While machine-readable medium 1022 is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions 1024). The term “machine-readable medium” shall also be taken to include any medium that is capable of storing instructions (e.g., instructions 1024) for execution by the machine and that cause the machine to perform any one or more of the methodologies disclosed herein. The term “machine-readable medium” includes, but not be limited to, data repositories in the form of solid-state memories, optical media, and magnetic media.

The computer system 1000 also may include the one or more sensors 1025. Also note that a computing device may include only a subset of the components illustrated and described with FIG. 7. For example, an IoT device may only include a processor 1002, a small storage unit 1016, a main memory 1004, a visual interface 1010, a network interface device 1020, and a sensor 1025.

VI. Additional Considerations

It is to be understood that the figures and descriptions of the present disclosure have been simplified to illustrate elements that are relevant for a clear understanding of the present disclosure, while eliminating, for the purpose of clarity, many other elements found in a typical system. Those of ordinary skill in the art may recognize that other elements and/or steps are desirable and/or required in implementing the present disclosure. However, because such elements and steps are well known in the art, and because they do not facilitate a better understanding of the present disclosure, a discussion of such elements and steps is not provided herein. The disclosure herein is directed to all such variations and modifications to such elements and methods known to those skilled in the art.

Some portions of above description describe the embodiments in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as engines, without loss of generality. The described operations and their associated engines may be embodied in software, firmware, hardware, or any combinations thereof.

As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the disclosed innovations. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.

While particular embodiments and applications have been illustrated and described, it is to be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims. 

What is claimed is:
 1. A method comprising: receiving, at a remote server, sensor data describing movement of a joint of a user covered by a plyowrap, wherein the sensor data is collected by an array of tension sensors embedded into the plyowrap across one or more horizontal axes and one or more vertical axes; inputting the received sensor data to a machine learning model to generate a prediction of the performance of the joint, wherein the machine-learned model is trained using a training dataset of historical sensor data collected from a population of users, each entry of the training data comprising historical sensor data collected for a joint and labeled with an identifier of the joint and a known movement of the joint; categorizing, based on the predicted performance of the joint, a risk level for biomechanical compromise of the joint; determining adjustment parameters for the brace based on the categorized risk level for biomechanical compromise of the joint; and transmitting, to a local controller coupled to the plyowrap, instructions to adjust structural properties of the brace based on the determined adjustment parameters, wherein the instructions activate electroactive gels in fluid chambers of the plyowrap to adjust the pliability of the plyowrap.
 2. The method of claim 1, wherein the machine-learned model is iteratively re-trained as the training dataset is updated with new sensor data collected from new users beginning to wear a plyowrap and existing users continuing to wear a plyowrap.
 3. The method of claim 1, wherein the predicted performance of the joint output by the model is a predicted range of motion of the joint, the method further comprising: comparing the predicted range of motion of the joint to an expected range of motion of the joint; and determining, responsive to the predicted range of motion differing from the expected range of motion by more than a threshold deviation, that the categorization of the risk level of the joint satisfies a threshold for potential biomechanical compromise.
 4. The method of claim 1, wherein determining adjustment parameters for the plyowrap further comprises: identifying a subset of fluid chambers of the plyowrap based on a model of properly performing joint and a model of an improperly performing joint; and generating instructions to adjust structural properties of the brace, wherein the generated instructions activate electroactive gels in the subset of fluid chambers to movement of the joint.
 5. The method of claim 1, wherein determining adjustment parameters for the plyowrap further comprises: detecting, by a tension sensor of the tension sensor array, an external impact to the joint; identifying a subset of fluid chambers of the plyowrap based on a model of properly performing joint and a model of an improperly performing joint; and generating instructions to adjust structural properties of the plyowrap, wherein the generated instructions activate electroactive gels in the subset of fluid chambers to support the joint against the detected external impact.
 6. The method of claim 1, wherein determining adjustment parameters for the plyowrap further comprises: generating instructions for improving performance of the joint by applying a substance at the joint, wherein the instructions activate a fluid chamber of plyowrap to deliver a substance to the user; or generating instructions for improving performance of joint by dispensing nutrients to the user, wherein the instructions activate a nutrition chamber of the wearable brace to deliver a nutrient supplement to the user.
 7. The method of claim 1, further comprising: generating instructions for improving performance of the joint by applying a substance at the joint, wherein the instructions actuate a delivery pump coupled to the wearable brace to deliver the substance to the user.
 8. A non-transitory computer readable storage medium comprising stored instructions, which when executed by at least one processor, cause the processor to: receive, at a remote server, sensor data describing movement of a joint of a user covered by a plyowrap, wherein the sensor data is collected by an array of tension sensors embedded into the plyowrap across one or more horizontal axes and one or more vertical axes; input the received sensor data to a machine learning model to generate a prediction of the performance of the joint, wherein the machine-learned model is trained using a training dataset of historical sensor data collected from a population of users, each entry of the training data comprising historical sensor data collected for a joint and labeled with an identifier of the joint and a known movement of the joint; categorize, based on the predicted performance of the joint, a risk level for biomechanical compromise of the joint; determine adjustment parameters for the brace based on the categorized risk level for biomechanical compromise of the joint; and transmit, to a local controller coupled to the plyowrap, instructions to adjust structural properties of the brace based on the determined adjustment parameters, wherein the instructions activate electroactive gels in fluid chambers of the plyowrap to adjust the pliability of the plyowrap.
 9. The non-transitory computer readable medium of claim 8, wherein the machine-learned model is iteratively re-trained as the training dataset is updated with new sensor data collected from new users beginning to wear a plyowrap and existing users continuing to wear a plyowrap.
 10. The non-transitory computer readable medium of claim 8, wherein the predicted performance of the joint output by the model is a predicted range of motion of the joint, the instructions further causing the processor to: compare the predicted range of motion of the joint to an expected range of motion of the joint; and determine, responsive to the predicted range of motion differing from the expected range of motion by more than a threshold deviation, that the categorization of the risk level of the joint satisfies a threshold for potential biomechanical compromise.
 11. The non-transitory computer readable medium of claim 8, wherein determining adjustment parameters for the plyowrap further causes the processor to: identify a subset of fluid chambers of the plyowrap based on a model of properly performing joint and a model of an improperly performing joint; and generate instructions to adjust structural properties of the brace, wherein the generated instructions activate electroactive gels in the subset of fluid chambers to movement of the joint.
 12. The non-transitory computer readable medium of claim 8, wherein determining adjustment parameters for the joint further causes the processor to: detect, by a tension sensor of the tension sensor array, an external impact to the joint; identify a subset of fluid chambers of the plyowrap based on a model of properly performing joint and a model of an improperly performing joint; and generate instructions to adjust structural properties of the plyowrap, wherein the generated instructions activate electroactive gels in the subset of fluid chambers to support the joint against the detected external impact.
 13. The non-transitory computer readable medium of claim 8, wherein determining adjustment parameters for the wearable brace further causes the processor to: generate instructions for improving performance of the joint by applying a substance at the joint, wherein the instructions activate a fluid chamber of plyowrap to deliver a substance to the user; or generate instructions for improving performance of joint by dispensing nutrients to the user, wherein the instructions activate a nutrition chamber of the wearable brace to deliver a nutrient supplement to the user.
 14. The non-transitory computer readable medium of claim 8, wherein determining adjustment parameters for the wearable brace further causes the processor to: generate instructions for improving performance of the joint by applying a substance at the joint, wherein the instructions actuate a delivery pump coupled to the wearable brace to deliver the substance to the user.
 15. A brace feedback system comprising: a joint performance evaluator configured to: receive, at a remote server, sensor data describing movement of a joint of a user covered by a plyowrap, wherein the sensor data is collected by an array of tension sensors embedded into the plyowrap across one or more horizontal axes and one or more vertical axes; input the received sensor data to a machine learning model to generate a prediction of the performance of the joint, wherein the machine-learned model is trained using a training dataset of historical sensor data collected from a population of users, each entry of the training data comprising historical sensor data collected for a joint and labeled with an identifier of the joint and a known movement of the joint; categorize, based on the predicted performance of the joint, a risk level for biomechanical compromise of the joint; a joint stabilization module configured to: determine adjustment parameters for the brace based on the categorized risk level for biomechanical compromise of the joint; and transmit, to a local controller coupled to the plyowrap, instructions to adjust structural properties of the brace based on the determined adjustment parameters, wherein the instructions activate electroactive gels in fluid chambers of the plyowrap to adjust the pliability of the plyowrap.
 16. The brace feedback system of claim 15, wherein the machine-learned model is iteratively re-trained as the training dataset is updated with new sensor data collected from new users beginning to wear a plyowrap and existing users continuing to wear a plyowrap.
 17. The brace feedback system of claim 15, wherein the predicted performance of the joint output by the model is a predicted range of motion of the joint, wherein the joint performance evaluator is further configured to: compare the predicted range of motion of the joint to an expected range of motion of the joint; and determine, responsive to the predicted range of motion differing from the expected range of motion by more than a threshold deviation, that the categorization of the risk level of the joint satisfies a threshold for potential biomechanical compromise.
 18. The brace feedback system of claim 15, wherein the joint stabilization module is further configured to: identify a subset of fluid chambers of the plyowrap based on a model of properly performing joint and a model of an improperly performing joint; and generate instructions to adjust structural properties of the brace, wherein the generated instructions activate electroactive gels in the subset of fluid chambers to movement of the joint.
 19. The brace feedback system of claim 15, wherein generating instructions for improving performance of the joint by adjusting structural properties of the wearable brace further cause the joint stabilization module to: detect, by a tension sensor of the tension sensor array, an external impact to the joint; identify a subset of fluid chambers of the plyowrap based on a model of properly performing joint and a model of an improperly performing joint; and generate instructions to adjust structural properties of the plyowrap, wherein the generated instructions activate electroactive gels in the subset of fluid chambers to support the joint against the detected external impact.
 20. The brace feedback system of claim 15, wherein the joint stabilization module is further configured to: generate instructions for improving performance of the joint by applying a substance at the joint, wherein the instructions activate a fluid chamber of the wearable brace to deliver a substance to the user; generate instructions for improving performance of joint by dispensing nutrients to the user, wherein the instructions activate a nutrition chamber of the wearable brace to deliver a nutrient supplement to the user; or generate instructions for improving performance of the joint by applying a substance at the joint, wherein the instructions actuate a delivery pump coupled to the wearable brace to deliver the substance to the user. 